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1,390 claims from 864 papers are on the record. 46 have been checked so far; the other 1,344 have no check with a result yet.

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Keyword: band gap prediction ×Clear all

2 claims from 2 papers

1. Materials Science › Machine Learning in Materials Science

   Machine learning bandgaps of double perovskites

   Pilania, Mannodi‐Kanakkithodi, Uberuaga, Ramprasad, Gubernatis and Lookman · Scientific Reports · 2016

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   1. Unchecked“After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors.”

2. Materials Science › Machine Learning in Materials Science

   Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validation

   Xiong, Cui, Liu, Zhao, Hu and Hu · Computational Materials Science · 2019

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   1. UncheckedMachine learning models that score well under standard cross-validation have very low explorative power, according to the authors' k-fold forward cross-validation method.“Our results show that even though current machine learning models can achieve good results when evaluated with traditional CV, their explorative power is actually very low as shown by our proposed km FCV evaluation method and the proposed exploration accuracy.”